@article{10272/27477,
year = {2025},
url = {https://hdl.handle.net/10272/27477},
abstract = {Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and temperature data, performs local logging, and communicates wirelessly. An automated spectral band segmentation framework is introduced, comparing equal-energy, linear-width, nonlinear, clustering, and peak–valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers—transformer autoencoders, GANomaly, and Isolation Forest—are evaluated on these weighted spectral features. Experiments conducted on a custom pump test bench with controlled anomaly severities demonstrate strong anomaly classification performance across multiple configurations, supported by detailed threshold-characterization metrics. Among 150 model–segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved ≥90% accuracy, and the lowest-performing setup maintained 81.8% accuracy. The proposed end-to-end solution reduces the downtime, lowers maintenance costs, and extends the asset life, offering a scalable, predictive maintenance approach for diverse industrial settings.},
organization = {This work was supported by research project Agricultura Sostenible de Cítricos con Inteligencia Artificial (0085_ATTENTIA_5_E), Programa de Cooperación Interreg España-Portugal (POCTEP) 2021–2027.},
publisher = {MDPI},
title = {Non-Intrusive Low-Cost IoT-Based Hardware System for Sustainable Predictive Maintenance of Industrial Pump Systems},
doi = {10.3390/electronics14142913},
author = {Duarte Brito, Sérgio and Azinheira, Gonçalo José and Semião, Jorge and Sousa, Nelson Manuel and Pérez Litrán, Salvador},
}